An improved Grey Wolf Optimization based heuristic initialization algorithm for feature selection in P2P lending default prediction
<p>This research addressed the problem of irrelevant feature and high-dimensional data in Peer-to-Peer (P2P) lending, which made the process hard to predict default accurately. By focusing on the right feature, the model performed better while irrelevant feature just added noise and lower accu...
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2025
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| Summary: | <p>This research addressed the problem of irrelevant feature and high-dimensional data in Peer-to-Peer (P2P) lending, which made the process hard to predict default accurately. By focusing on the right feature, the model performed better while irrelevant feature just added noise and lower accuracy. High-dimensional data created a problem known as ‘curse of dimensionality.’ This issue increased computational complexity and reduced ability of the model to work well with new data. Therefore, picking the right feature was essential to better predictions and faster computing. In this research, the finding used Grey Wolf Optimization (GWO) algorithm for selecting feature. However, GWO had flaw, starting with suboptimal initial solutions, which were not often the best option. The research incorporated Ant Colony Optimization (ACO) algorithm for better population initialization to overcome this limitation. ACO used pheromone trails and heuristics to find good starting solutions, thereby improving performance of GWO. During the analysis, the proposed model known as improved GWO+ACO, was tested with various configurations of search agents (50, 100, and 250). The tests showed that improved GWO+ACO was better than the standard GWO in terms of accuracy and stability across all configurations. Improved GWO+ACO maintained a steady accuracy of 91% at all search agent levels. In comparison, standard GWO had varying accuracy, including 85%, 90%, and 91% with 50, 100, as well as 250 search agents, respectively. Generally, using ACO for the starting point made the model less dependent on the number of search agents and also improved the optimization process significantly. Therefore, this method proved to be more effective in handling complex P2P lending data and improving default prediction accuracy.</p> |
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